Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations93
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.5 KiB
Average record size in memory225.4 B

Variable types

Numeric19
Categorical6
Text2
Boolean1

Alerts

Cylinders is highly overall correlated with EngineSize and 3 other fieldsHigh correlation
EngineSize is highly overall correlated with Cylinders and 18 other fieldsHigh correlation
Fuel.tank.capacity is highly overall correlated with EngineSize and 15 other fieldsHigh correlation
Horsepower is highly overall correlated with Cylinders and 13 other fieldsHigh correlation
Length is highly overall correlated with EngineSize and 16 other fieldsHigh correlation
Luggage.room is highly overall correlated with EngineSize and 7 other fieldsHigh correlation
MPG.city is highly overall correlated with EngineSize and 13 other fieldsHigh correlation
MPG.highway is highly overall correlated with EngineSize and 12 other fieldsHigh correlation
Man.trans.avail is highly overall correlated with Cylinders and 9 other fieldsHigh correlation
Manufacturer is highly overall correlated with Origin and 1 other fieldsHigh correlation
Max.Price is highly overall correlated with EngineSize and 12 other fieldsHigh correlation
Min.Price is highly overall correlated with EngineSize and 13 other fieldsHigh correlation
Origin is highly overall correlated with Manufacturer and 1 other fieldsHigh correlation
Passengers is highly overall correlated with EngineSize and 7 other fieldsHigh correlation
Price is highly overall correlated with EngineSize and 12 other fieldsHigh correlation
RPM is highly overall correlated with EngineSize and 2 other fieldsHigh correlation
Rear.seat.room is highly overall correlated with EngineSize and 9 other fieldsHigh correlation
Rev.per.mile is highly overall correlated with EngineSize and 13 other fieldsHigh correlation
Turn.circle is highly overall correlated with EngineSize and 15 other fieldsHigh correlation
Type is highly overall correlated with Fuel.tank.capacity and 4 other fieldsHigh correlation
Weight is highly overall correlated with Cylinders and 18 other fieldsHigh correlation
Wheelbase is highly overall correlated with EngineSize and 17 other fieldsHigh correlation
Width is highly overall correlated with EngineSize and 17 other fieldsHigh correlation
id is highly overall correlated with Manufacturer and 1 other fieldsHigh correlation
id is uniformly distributed Uniform
id has unique values Unique
Model has unique values Unique
Make has unique values Unique

Reproduction

Analysis started2025-04-04 07:58:21.470137
Analysis finished2025-04-04 07:58:50.587573
Duration29.12 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:50.668086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.6
Q124
median47
Q370
95-th percentile88.4
Maximum93
Range92
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.990739
Coefficient of variation (CV)0.57427105
Kurtosis-1.2
Mean47
Median Absolute Deviation (MAD)23
Skewness0
Sum4371
Variance728.5
MonotonicityStrictly increasing
2025-04-04T13:28:50.774598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
60 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
Other values (83) 83
89.2%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%

Manufacturer
Categorical

High correlation 

Distinct32
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size876.0 B
Chevrolet
Ford
Dodge
 
6
Pontiac
 
5
Mazda
 
5
Other values (27)
61 

Length

Max length13
Median length9
Mean length6.516129
Min length3

Characters and Unicode

Total characters606
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)7.5%

Sample

1st rowAcura
2nd rowAcura
3rd rowAudi
4th rowAudi
5th rowBMW

Common Values

ValueCountFrequency (%)
Chevrolet 8
 
8.6%
Ford 8
 
8.6%
Dodge 6
 
6.5%
Pontiac 5
 
5.4%
Mazda 5
 
5.4%
Oldsmobile 4
 
4.3%
Hyundai 4
 
4.3%
Nissan 4
 
4.3%
Toyota 4
 
4.3%
Buick 4
 
4.3%
Other values (22) 41
44.1%

Length

2025-04-04T13:28:50.873111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chevrolet 8
 
8.6%
ford 8
 
8.6%
dodge 6
 
6.5%
pontiac 5
 
5.4%
mazda 5
 
5.4%
toyota 4
 
4.3%
volkswagen 4
 
4.3%
buick 4
 
4.3%
nissan 4
 
4.3%
hyundai 4
 
4.3%
Other values (22) 41
44.1%

Most occurring characters

ValueCountFrequency (%)
o 55
 
9.1%
e 49
 
8.1%
a 48
 
7.9%
i 37
 
6.1%
d 36
 
5.9%
r 34
 
5.6%
l 34
 
5.6%
n 29
 
4.8%
u 28
 
4.6%
s 27
 
4.5%
Other values (32) 229
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 55
 
9.1%
e 49
 
8.1%
a 48
 
7.9%
i 37
 
6.1%
d 36
 
5.9%
r 34
 
5.6%
l 34
 
5.6%
n 29
 
4.8%
u 28
 
4.6%
s 27
 
4.5%
Other values (32) 229
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 55
 
9.1%
e 49
 
8.1%
a 48
 
7.9%
i 37
 
6.1%
d 36
 
5.9%
r 34
 
5.6%
l 34
 
5.6%
n 29
 
4.8%
u 28
 
4.6%
s 27
 
4.5%
Other values (32) 229
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 55
 
9.1%
e 49
 
8.1%
a 48
 
7.9%
i 37
 
6.1%
d 36
 
5.9%
r 34
 
5.6%
l 34
 
5.6%
n 29
 
4.8%
u 28
 
4.6%
s 27
 
4.5%
Other values (32) 229
37.8%

Model
Text

Unique 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size876.0 B
2025-04-04T13:28:51.050884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.2473118
Min length2

Characters and Unicode

Total characters581
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)100.0%

Sample

1st rowIntegra
2nd rowLegend
3rd row90
4th row100
5th row535i
ValueCountFrequency (%)
integra 1
 
1.1%
scoupe 1
 
1.1%
90 1
 
1.1%
100 1
 
1.1%
535i 1
 
1.1%
century 1
 
1.1%
lesabre 1
 
1.1%
roadmaster 1
 
1.1%
riviera 1
 
1.1%
deville 1
 
1.1%
Other values (83) 83
89.2%
2025-04-04T13:28:51.347555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 56
 
9.6%
e 51
 
8.8%
r 47
 
8.1%
i 40
 
6.9%
t 36
 
6.2%
o 33
 
5.7%
n 26
 
4.5%
C 21
 
3.6%
l 20
 
3.4%
s 18
 
3.1%
Other values (42) 233
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 56
 
9.6%
e 51
 
8.8%
r 47
 
8.1%
i 40
 
6.9%
t 36
 
6.2%
o 33
 
5.7%
n 26
 
4.5%
C 21
 
3.6%
l 20
 
3.4%
s 18
 
3.1%
Other values (42) 233
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 56
 
9.6%
e 51
 
8.8%
r 47
 
8.1%
i 40
 
6.9%
t 36
 
6.2%
o 33
 
5.7%
n 26
 
4.5%
C 21
 
3.6%
l 20
 
3.4%
s 18
 
3.1%
Other values (42) 233
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 56
 
9.6%
e 51
 
8.8%
r 47
 
8.1%
i 40
 
6.9%
t 36
 
6.2%
o 33
 
5.7%
n 26
 
4.5%
C 21
 
3.6%
l 20
 
3.4%
s 18
 
3.1%
Other values (42) 233
40.1%

Type
Categorical

High correlation 

Distinct6
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size876.0 B
Midsize
22 
Small
21 
Compact
16 
Sporty
14 
Large
11 

Length

Max length7
Median length6
Mean length5.7741935
Min length3

Characters and Unicode

Total characters537
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall
2nd rowMidsize
3rd rowCompact
4th rowMidsize
5th rowMidsize

Common Values

ValueCountFrequency (%)
Midsize 22
23.7%
Small 21
22.6%
Compact 16
17.2%
Sporty 14
15.1%
Large 11
11.8%
Van 9
9.7%

Length

2025-04-04T13:28:51.467566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T13:28:51.571120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
midsize 22
23.7%
small 21
22.6%
compact 16
17.2%
sporty 14
15.1%
large 11
11.8%
van 9
9.7%

Most occurring characters

ValueCountFrequency (%)
a 57
 
10.6%
i 44
 
8.2%
l 42
 
7.8%
m 37
 
6.9%
S 35
 
6.5%
e 33
 
6.1%
t 30
 
5.6%
o 30
 
5.6%
p 30
 
5.6%
r 25
 
4.7%
Other values (11) 174
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 57
 
10.6%
i 44
 
8.2%
l 42
 
7.8%
m 37
 
6.9%
S 35
 
6.5%
e 33
 
6.1%
t 30
 
5.6%
o 30
 
5.6%
p 30
 
5.6%
r 25
 
4.7%
Other values (11) 174
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 57
 
10.6%
i 44
 
8.2%
l 42
 
7.8%
m 37
 
6.9%
S 35
 
6.5%
e 33
 
6.1%
t 30
 
5.6%
o 30
 
5.6%
p 30
 
5.6%
r 25
 
4.7%
Other values (11) 174
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 57
 
10.6%
i 44
 
8.2%
l 42
 
7.8%
m 37
 
6.9%
S 35
 
6.5%
e 33
 
6.1%
t 30
 
5.6%
o 30
 
5.6%
p 30
 
5.6%
r 25
 
4.7%
Other values (11) 174
32.4%

Min.Price
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.125806
Minimum6.7
Maximum45.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:51.679862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile7.36
Q110.8
median14.7
Q320.3
95-th percentile34.48
Maximum45.4
Range38.7
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation8.746029
Coefficient of variation (CV)0.51069297
Kurtosis1.0194187
Mean17.125806
Median Absolute Deviation (MAD)5.2
Skewness1.1829892
Sum1592.7
Variance76.493022
MonotonicityNot monotonic
2025-04-04T13:28:51.796375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 3
 
3.2%
14.2 3
 
3.2%
7.3 2
 
2.2%
13 2
 
2.2%
8.7 2
 
2.2%
7.9 2
 
2.2%
14.5 2
 
2.2%
14.7 2
 
2.2%
13.4 2
 
2.2%
19.5 2
 
2.2%
Other values (69) 71
76.3%
ValueCountFrequency (%)
6.7 1
 
1.1%
6.8 1
 
1.1%
6.9 1
 
1.1%
7.3 2
2.2%
7.4 1
 
1.1%
7.7 1
 
1.1%
7.8 1
 
1.1%
7.9 2
2.2%
8.2 1
 
1.1%
8.4 3
3.2%
ValueCountFrequency (%)
45.4 1
1.1%
43.8 1
1.1%
37.5 1
1.1%
34.7 1
1.1%
34.6 1
1.1%
34.4 1
1.1%
33.3 1
1.1%
33 1
1.1%
32.5 1
1.1%
30.8 1
1.1%

Price
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.509677
Minimum7.4
Maximum61.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:51.911886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile8.52
Q112.2
median17.7
Q323.3
95-th percentile36.74
Maximum61.9
Range54.5
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation9.6594296
Coefficient of variation (CV)0.49510965
Kurtosis3.4291226
Mean19.509677
Median Absolute Deviation (MAD)5.6
Skewness1.5330819
Sum1814.4
Variance93.304579
MonotonicityNot monotonic
2025-04-04T13:28:52.021399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.9 3
 
3.2%
10 2
 
2.2%
19.5 2
 
2.2%
11.3 2
 
2.2%
15.7 2
 
2.2%
22.7 2
 
2.2%
8.4 2
 
2.2%
19.1 2
 
2.2%
18.4 2
 
2.2%
11.1 2
 
2.2%
Other values (71) 72
77.4%
ValueCountFrequency (%)
7.4 1
1.1%
8 1
1.1%
8.3 1
1.1%
8.4 2
2.2%
8.6 1
1.1%
9 1
1.1%
9.1 1
1.1%
9.2 1
1.1%
9.8 1
1.1%
10 2
2.2%
ValueCountFrequency (%)
61.9 1
1.1%
47.9 1
1.1%
40.1 1
1.1%
38 1
1.1%
37.7 1
1.1%
36.1 1
1.1%
35.2 1
1.1%
34.7 1
1.1%
34.3 1
1.1%
33.9 1
1.1%

Max.Price
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.898925
Minimum7.9
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:52.126910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.9
5-th percentile9.74
Q114.7
median19.6
Q325.3
95-th percentile39.82
Maximum80
Range72.1
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation11.030457
Coefficient of variation (CV)0.50369856
Kurtosis7.4394262
Mean21.898925
Median Absolute Deviation (MAD)5.4
Skewness2.0338587
Sum2036.6
Variance121.67098
MonotonicityNot monotonic
2025-04-04T13:28:52.240424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.4 3
 
3.2%
22.7 3
 
3.2%
21.7 3
 
3.2%
21.2 3
 
3.2%
18.3 2
 
2.2%
11 2
 
2.2%
12.9 2
 
2.2%
14.9 2
 
2.2%
10 2
 
2.2%
9.5 2
 
2.2%
Other values (69) 69
74.2%
ValueCountFrequency (%)
7.9 1
1.1%
9.1 1
1.1%
9.2 1
1.1%
9.5 2
2.2%
9.9 1
1.1%
10 2
2.2%
10.6 1
1.1%
11 2
2.2%
11.3 1
1.1%
11.4 1
1.1%
ValueCountFrequency (%)
80 1
1.1%
50.4 1
1.1%
44.6 1
1.1%
42.7 1
1.1%
41.5 1
1.1%
38.7 1
1.1%
37.8 1
1.1%
37.1 1
1.1%
36.3 1
1.1%
36.2 1
1.1%

MPG.city
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.365591
Minimum15
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:52.335931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.6
Q118
median21
Q325
95-th percentile31.4
Maximum46
Range31
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6198115
Coefficient of variation (CV)0.25127042
Kurtosis4.0043059
Mean22.365591
Median Absolute Deviation (MAD)3
Skewness1.7044301
Sum2080
Variance31.582281
MonotonicityNot monotonic
2025-04-04T13:28:52.422436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
18 12
12.9%
19 10
10.8%
17 8
8.6%
20 8
8.6%
23 8
8.6%
22 7
7.5%
25 6
 
6.5%
29 6
 
6.5%
21 6
 
6.5%
24 5
 
5.4%
Other values (11) 17
18.3%
ValueCountFrequency (%)
15 2
 
2.2%
16 3
 
3.2%
17 8
8.6%
18 12
12.9%
19 10
10.8%
20 8
8.6%
21 6
6.5%
22 7
7.5%
23 8
8.6%
24 5
5.4%
ValueCountFrequency (%)
46 1
 
1.1%
42 1
 
1.1%
39 1
 
1.1%
33 1
 
1.1%
32 1
 
1.1%
31 2
 
2.2%
30 1
 
1.1%
29 6
6.5%
28 2
 
2.2%
26 2
 
2.2%

MPG.highway
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.086022
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:52.508943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q126
median28
Q331
95-th percentile37.4
Maximum50
Range30
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.331726
Coefficient of variation (CV)0.18330888
Kurtosis2.6142006
Mean29.086022
Median Absolute Deviation (MAD)3
Skewness1.2298967
Sum2705
Variance28.427302
MonotonicityNot monotonic
2025-04-04T13:28:52.599448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
26 11
11.8%
28 10
10.8%
30 9
9.7%
25 8
 
8.6%
31 7
 
7.5%
33 7
 
7.5%
29 6
 
6.5%
27 6
 
6.5%
23 4
 
4.3%
24 4
 
4.3%
Other values (12) 21
22.6%
ValueCountFrequency (%)
20 2
 
2.2%
21 2
 
2.2%
22 2
 
2.2%
23 4
 
4.3%
24 4
 
4.3%
25 8
8.6%
26 11
11.8%
27 6
6.5%
28 10
10.8%
29 6
6.5%
ValueCountFrequency (%)
50 1
 
1.1%
46 1
 
1.1%
43 1
 
1.1%
41 1
 
1.1%
38 1
 
1.1%
37 3
3.2%
36 3
3.2%
34 3
3.2%
33 7
7.5%
32 1
 
1.1%

AirBags
Categorical

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size876.0 B
Driver only
46 
None
33 
Driver & Passenger
14 

Length

Max length18
Median length11
Mean length9.5698925
Min length4

Characters and Unicode

Total characters890
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowDriver & Passenger
3rd rowDriver only
4th rowDriver only
5th rowDriver only

Common Values

ValueCountFrequency (%)
Driver only 46
49.5%
None 33
35.5%
Driver & Passenger 14
 
15.1%

Length

2025-04-04T13:28:52.698955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T13:28:52.778461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
driver 60
35.9%
only 46
27.5%
none 33
19.8%
14
 
8.4%
passenger 14
 
8.4%

Most occurring characters

ValueCountFrequency (%)
r 134
15.1%
e 121
13.6%
n 93
10.4%
o 79
8.9%
74
8.3%
D 60
6.7%
i 60
6.7%
v 60
6.7%
l 46
 
5.2%
y 46
 
5.2%
Other values (6) 117
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 134
15.1%
e 121
13.6%
n 93
10.4%
o 79
8.9%
74
8.3%
D 60
6.7%
i 60
6.7%
v 60
6.7%
l 46
 
5.2%
y 46
 
5.2%
Other values (6) 117
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 134
15.1%
e 121
13.6%
n 93
10.4%
o 79
8.9%
74
8.3%
D 60
6.7%
i 60
6.7%
v 60
6.7%
l 46
 
5.2%
y 46
 
5.2%
Other values (6) 117
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 134
15.1%
e 121
13.6%
n 93
10.4%
o 79
8.9%
74
8.3%
D 60
6.7%
i 60
6.7%
v 60
6.7%
l 46
 
5.2%
y 46
 
5.2%
Other values (6) 117
13.1%

DriveTrain
Categorical

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size876.0 B
Front
67 
Rear
16 
4WD
10 

Length

Max length5
Median length5
Mean length4.6129032
Min length3

Characters and Unicode

Total characters429
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFront
2nd rowFront
3rd rowFront
4th rowFront
5th rowRear

Common Values

ValueCountFrequency (%)
Front 67
72.0%
Rear 16
 
17.2%
4WD 10
 
10.8%

Length

2025-04-04T13:28:52.873967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T13:28:52.957968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
front 67
72.0%
rear 16
 
17.2%
4wd 10
 
10.8%

Most occurring characters

ValueCountFrequency (%)
r 83
19.3%
F 67
15.6%
o 67
15.6%
n 67
15.6%
t 67
15.6%
R 16
 
3.7%
e 16
 
3.7%
a 16
 
3.7%
4 10
 
2.3%
W 10
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 83
19.3%
F 67
15.6%
o 67
15.6%
n 67
15.6%
t 67
15.6%
R 16
 
3.7%
e 16
 
3.7%
a 16
 
3.7%
4 10
 
2.3%
W 10
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 83
19.3%
F 67
15.6%
o 67
15.6%
n 67
15.6%
t 67
15.6%
R 16
 
3.7%
e 16
 
3.7%
a 16
 
3.7%
4 10
 
2.3%
W 10
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 83
19.3%
F 67
15.6%
o 67
15.6%
n 67
15.6%
t 67
15.6%
R 16
 
3.7%
e 16
 
3.7%
a 16
 
3.7%
4 10
 
2.3%
W 10
 
2.3%

Cylinders
Categorical

High correlation 

Distinct6
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size876.0 B
4
49 
6
31 
8
3
 
3
5
 
2

Length

Max length6
Median length1
Mean length1.0537634
Min length1

Characters and Unicode

Total characters98
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row4
2nd row6
3rd row6
4th row6
5th row4

Common Values

ValueCountFrequency (%)
4 49
52.7%
6 31
33.3%
8 7
 
7.5%
3 3
 
3.2%
5 2
 
2.2%
rotary 1
 
1.1%

Length

2025-04-04T13:28:53.041472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T13:28:53.120978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 49
52.7%
6 31
33.3%
8 7
 
7.5%
3 3
 
3.2%
5 2
 
2.2%
rotary 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
4 49
50.0%
6 31
31.6%
8 7
 
7.1%
3 3
 
3.1%
5 2
 
2.0%
r 2
 
2.0%
o 1
 
1.0%
t 1
 
1.0%
a 1
 
1.0%
y 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 49
50.0%
6 31
31.6%
8 7
 
7.1%
3 3
 
3.1%
5 2
 
2.0%
r 2
 
2.0%
o 1
 
1.0%
t 1
 
1.0%
a 1
 
1.0%
y 1
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 49
50.0%
6 31
31.6%
8 7
 
7.1%
3 3
 
3.1%
5 2
 
2.0%
r 2
 
2.0%
o 1
 
1.0%
t 1
 
1.0%
a 1
 
1.0%
y 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 49
50.0%
6 31
31.6%
8 7
 
7.1%
3 3
 
3.1%
5 2
 
2.0%
r 2
 
2.0%
o 1
 
1.0%
t 1
 
1.0%
a 1
 
1.0%
y 1
 
1.0%

EngineSize
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6677419
Minimum1
Maximum5.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:53.204484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.42
Q11.8
median2.4
Q33.3
95-th percentile4.6
Maximum5.7
Range4.7
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.037363
Coefficient of variation (CV)0.38885433
Kurtosis0.38102491
Mean2.6677419
Median Absolute Deviation (MAD)0.6
Skewness0.85941842
Sum248.1
Variance1.076122
MonotonicityNot monotonic
2025-04-04T13:28:53.299230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3 11
 
11.8%
2.2 10
 
10.8%
3.8 8
 
8.6%
1.5 7
 
7.5%
1.8 7
 
7.5%
2.3 6
 
6.5%
1.6 5
 
5.4%
2.5 4
 
4.3%
2 4
 
4.3%
2.4 3
 
3.2%
Other values (16) 28
30.1%
ValueCountFrequency (%)
1 1
 
1.1%
1.2 1
 
1.1%
1.3 3
 
3.2%
1.5 7
7.5%
1.6 5
5.4%
1.8 7
7.5%
1.9 1
 
1.1%
2 4
 
4.3%
2.1 1
 
1.1%
2.2 10
10.8%
ValueCountFrequency (%)
5.7 2
 
2.2%
5 1
 
1.1%
4.9 1
 
1.1%
4.6 3
 
3.2%
4.5 1
 
1.1%
4.3 1
 
1.1%
3.8 8
8.6%
3.5 2
 
2.2%
3.4 3
 
3.2%
3.3 2
 
2.2%

Horsepower
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.82796
Minimum55
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:53.396946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile78.2
Q1103
median140
Q3170
95-th percentile237
Maximum300
Range245
Interquartile range (IQR)67

Descriptive statistics

Standard deviation52.37441
Coefficient of variation (CV)0.36414624
Kurtosis1.1108826
Mean143.82796
Median Absolute Deviation (MAD)30
Skewness0.95172825
Sum13376
Variance2743.0788
MonotonicityNot monotonic
2025-04-04T13:28:53.511616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 7
 
7.5%
170 7
 
7.5%
140 5
 
5.4%
160 5
 
5.4%
92 5
 
5.4%
200 3
 
3.2%
130 3
 
3.2%
100 3
 
3.2%
300 2
 
2.2%
82 2
 
2.2%
Other values (47) 51
54.8%
ValueCountFrequency (%)
55 1
 
1.1%
63 1
 
1.1%
70 1
 
1.1%
73 1
 
1.1%
74 1
 
1.1%
81 2
 
2.2%
82 2
 
2.2%
85 1
 
1.1%
90 2
 
2.2%
92 5
5.4%
ValueCountFrequency (%)
300 2
2.2%
295 1
1.1%
278 1
1.1%
255 1
1.1%
225 1
1.1%
217 1
1.1%
214 1
1.1%
210 1
1.1%
208 1
1.1%
202 1
1.1%

RPM
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5280.6452
Minimum3800
Maximum6500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:53.608310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3800
5-th percentile4200
Q14800
median5200
Q35750
95-th percentile6000
Maximum6500
Range2700
Interquartile range (IQR)950

Descriptive statistics

Standard deviation596.73169
Coefficient of variation (CV)0.11300356
Kurtosis-0.40947898
Mean5280.6452
Median Absolute Deviation (MAD)400
Skewness-0.25853269
Sum491100
Variance356088.71
MonotonicityNot monotonic
2025-04-04T13:28:53.703072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6000 14
15.1%
4800 13
14.0%
5000 10
10.8%
5200 10
10.8%
5500 8
8.6%
5600 6
 
6.5%
5800 4
 
4.3%
5400 4
 
4.3%
4600 4
 
4.3%
4200 3
 
3.2%
Other values (14) 17
18.3%
ValueCountFrequency (%)
3800 1
 
1.1%
4000 2
 
2.2%
4100 1
 
1.1%
4200 3
 
3.2%
4400 1
 
1.1%
4500 1
 
1.1%
4600 4
 
4.3%
4800 13
14.0%
5000 10
10.8%
5100 1
 
1.1%
ValueCountFrequency (%)
6500 2
 
2.2%
6300 1
 
1.1%
6200 1
 
1.1%
6000 14
15.1%
5900 1
 
1.1%
5800 4
 
4.3%
5750 1
 
1.1%
5700 2
 
2.2%
5600 6
6.5%
5550 1
 
1.1%

Rev.per.mile
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2332.2043
Minimum1320
Maximum3755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:53.803817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1320
5-th percentile1543
Q11985
median2340
Q32565
95-th percentile3264
Maximum3755
Range2435
Interquartile range (IQR)580

Descriptive statistics

Standard deviation496.50653
Coefficient of variation (CV)0.21289152
Kurtosis0.22054386
Mean2332.2043
Median Absolute Deviation (MAD)275
Skewness0.28154602
Sum216895
Variance246518.73
MonotonicityNot monotonic
2025-04-04T13:28:53.908571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2505 3
 
3.2%
1690 3
 
3.2%
2325 2
 
2.2%
2220 2
 
2.2%
2340 2
 
2.2%
2595 2
 
2.2%
2665 2
 
2.2%
2380 2
 
2.2%
2335 2
 
2.2%
1805 2
 
2.2%
Other values (68) 71
76.3%
ValueCountFrequency (%)
1320 1
 
1.1%
1350 1
 
1.1%
1415 1
 
1.1%
1450 1
 
1.1%
1510 1
 
1.1%
1565 1
 
1.1%
1570 2
2.2%
1690 3
3.2%
1730 1
 
1.1%
1785 1
 
1.1%
ValueCountFrequency (%)
3755 1
1.1%
3505 1
1.1%
3375 1
1.1%
3360 1
1.1%
3285 1
1.1%
3250 1
1.1%
3150 1
1.1%
3130 1
1.1%
2915 1
1.1%
2910 1
1.1%

Man.trans.avail
Boolean

High correlation 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size225.0 B
True
61 
False
32 
ValueCountFrequency (%)
True 61
65.6%
False 32
34.4%
2025-04-04T13:28:53.988333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Fuel.tank.capacity
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.664516
Minimum9.2
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:54.067071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile11.58
Q114.5
median16.4
Q318.8
95-th percentile21.1
Maximum27
Range17.8
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.2793705
Coefficient of variation (CV)0.19678762
Kurtosis0.1272065
Mean16.664516
Median Absolute Deviation (MAD)2.1
Skewness0.1081462
Sum1549.8
Variance10.754271
MonotonicityNot monotonic
2025-04-04T13:28:54.170748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
20 9
 
9.7%
18 9
 
9.7%
13.2 8
 
8.6%
15.9 7
 
7.5%
18.5 6
 
6.5%
16 5
 
5.4%
11.9 4
 
4.3%
15.5 4
 
4.3%
15.2 3
 
3.2%
16.5 3
 
3.2%
Other values (28) 35
37.6%
ValueCountFrequency (%)
9.2 1
 
1.1%
10 1
 
1.1%
10.6 2
 
2.2%
11.1 1
 
1.1%
11.9 4
4.3%
12.4 2
 
2.2%
12.8 1
 
1.1%
13.2 8
8.6%
13.7 1
 
1.1%
14 1
 
1.1%
ValueCountFrequency (%)
27 1
 
1.1%
23 2
 
2.2%
22.5 1
 
1.1%
21.1 3
 
3.2%
21 1
 
1.1%
20.6 1
 
1.1%
20 9
9.7%
19.8 2
 
2.2%
19.6 1
 
1.1%
19.3 1
 
1.1%

Passengers
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0860215
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:54.258896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median5
Q36
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0389785
Coefficient of variation (CV)0.20428119
Kurtosis0.9361926
Mean5.0860215
Median Absolute Deviation (MAD)1
Skewness0.062516854
Sum473
Variance1.0794764
MonotonicityNot monotonic
2025-04-04T13:28:54.337558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 41
44.1%
4 23
24.7%
6 18
19.4%
7 8
 
8.6%
2 2
 
2.2%
8 1
 
1.1%
ValueCountFrequency (%)
2 2
 
2.2%
4 23
24.7%
5 41
44.1%
6 18
19.4%
7 8
 
8.6%
8 1
 
1.1%
ValueCountFrequency (%)
8 1
 
1.1%
7 8
 
8.6%
6 18
19.4%
5 41
44.1%
4 23
24.7%
2 2
 
2.2%

Length
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.2043
Minimum141
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:54.436292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile161.6
Q1174
median183
Q3192
95-th percentile205.4
Maximum219
Range78
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.602382
Coefficient of variation (CV)0.079705452
Kurtosis0.44925041
Mean183.2043
Median Absolute Deviation (MAD)9
Skewness-0.090094622
Sum17038
Variance213.22955
MonotonicityNot monotonic
2025-04-04T13:28:54.556114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 5
 
5.4%
190 5
 
5.4%
177 4
 
4.3%
175 4
 
4.3%
172 4
 
4.3%
188 4
 
4.3%
180 4
 
4.3%
187 3
 
3.2%
181 3
 
3.2%
179 3
 
3.2%
Other values (41) 54
58.1%
ValueCountFrequency (%)
141 1
1.1%
146 1
1.1%
151 1
1.1%
159 1
1.1%
161 1
1.1%
162 1
1.1%
163 1
1.1%
164 2
2.2%
166 2
2.2%
168 1
1.1%
ValueCountFrequency (%)
219 1
1.1%
216 1
1.1%
214 1
1.1%
212 1
1.1%
206 1
1.1%
205 1
1.1%
204 1
1.1%
203 2
2.2%
202 1
1.1%
201 1
1.1%

Wheelbase
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.94624
Minimum90
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:54.657867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile93.6
Q198
median103
Q3110
95-th percentile115
Maximum119
Range29
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8196736
Coefficient of variation (CV)0.065607701
Kurtosis-0.79745573
Mean103.94624
Median Absolute Deviation (MAD)5
Skewness0.11372684
Sum9667
Variance46.507948
MonotonicityNot monotonic
2025-04-04T13:28:54.750543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
103 9
 
9.7%
97 7
 
7.5%
98 6
 
6.5%
113 5
 
5.4%
101 5
 
5.4%
105 5
 
5.4%
111 5
 
5.4%
104 5
 
5.4%
110 5
 
5.4%
102 4
 
4.3%
Other values (17) 37
39.8%
ValueCountFrequency (%)
90 2
 
2.2%
93 3
3.2%
94 3
3.2%
95 1
 
1.1%
96 3
3.2%
97 7
7.5%
98 6
6.5%
99 3
3.2%
100 2
 
2.2%
101 5
5.4%
ValueCountFrequency (%)
119 1
 
1.1%
117 1
 
1.1%
116 2
 
2.2%
115 2
 
2.2%
114 2
 
2.2%
113 5
5.4%
112 2
 
2.2%
111 5
5.4%
110 5
5.4%
109 2
 
2.2%

Width
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.376344
Minimum60
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:54.835294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile63
Q167
median69
Q372
95-th percentile75.8
Maximum78
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7789865
Coefficient of variation (CV)0.054470822
Kurtosis-0.2464277
Mean69.376344
Median Absolute Deviation (MAD)3
Skewness0.26402738
Sum6452
Variance14.280739
MonotonicityNot monotonic
2025-04-04T13:28:55.219966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
67 15
16.1%
69 11
11.8%
74 11
11.8%
66 10
10.8%
68 7
7.5%
70 7
7.5%
72 7
7.5%
71 5
 
5.4%
63 5
 
5.4%
73 4
 
4.3%
Other values (6) 11
11.8%
ValueCountFrequency (%)
60 1
 
1.1%
63 5
 
5.4%
64 1
 
1.1%
65 3
 
3.2%
66 10
10.8%
67 15
16.1%
68 7
7.5%
69 11
11.8%
70 7
7.5%
71 5
 
5.4%
ValueCountFrequency (%)
78 3
 
3.2%
77 2
 
2.2%
75 1
 
1.1%
74 11
11.8%
73 4
 
4.3%
72 7
7.5%
71 5
5.4%
70 7
7.5%
69 11
11.8%
68 7
7.5%

Turn.circle
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.956989
Minimum32
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:55.311476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile34
Q137
median39
Q341
95-th percentile44
Maximum45
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2232645
Coefficient of variation (CV)0.082739057
Kurtosis-0.73220396
Mean38.956989
Median Absolute Deviation (MAD)2
Skewness-0.13356858
Sum3623
Variance10.389434
MonotonicityNot monotonic
2025-04-04T13:28:55.400982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
39 11
11.8%
40 10
10.8%
37 9
9.7%
41 9
9.7%
42 9
9.7%
36 9
9.7%
38 8
8.6%
43 7
7.5%
34 6
6.5%
44 4
 
4.3%
Other values (4) 11
11.8%
ValueCountFrequency (%)
32 2
 
2.2%
33 2
 
2.2%
34 6
6.5%
35 4
 
4.3%
36 9
9.7%
37 9
9.7%
38 8
8.6%
39 11
11.8%
40 10
10.8%
41 9
9.7%
ValueCountFrequency (%)
45 3
 
3.2%
44 4
 
4.3%
43 7
7.5%
42 9
9.7%
41 9
9.7%
40 10
10.8%
39 11
11.8%
38 8
8.6%
37 9
9.7%
36 9
9.7%

Rear.seat.room
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.82967
Minimum19
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:55.488489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile23.5
Q126
median27.5
Q330
95-th percentile32.3
Maximum36
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.956404
Coefficient of variation (CV)0.10623209
Kurtosis0.9798816
Mean27.82967
Median Absolute Deviation (MAD)1.5
Skewness0.079086061
Sum2588.1593
Variance8.7403249
MonotonicityNot monotonic
2025-04-04T13:28:55.581995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
26.5 9
 
9.7%
28 9
 
9.7%
30.5 8
 
8.6%
26 8
 
8.6%
27.5 8
 
8.6%
30 6
 
6.5%
27 5
 
5.4%
25 5
 
5.4%
28.5 5
 
5.4%
31 4
 
4.3%
Other values (15) 26
28.0%
ValueCountFrequency (%)
19 1
 
1.1%
20 1
 
1.1%
23 2
 
2.2%
23.5 3
 
3.2%
24 2
 
2.2%
24.5 2
 
2.2%
25 5
5.4%
25.5 1
 
1.1%
26 8
8.6%
26.5 9
9.7%
ValueCountFrequency (%)
36 1
 
1.1%
35 2
 
2.2%
34 1
 
1.1%
33.5 1
 
1.1%
31.5 3
 
3.2%
31 4
4.3%
30.5 8
8.6%
30 6
6.5%
29.5 3
 
3.2%
29 1
 
1.1%

Luggage.room
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.890244
Minimum6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:55.667500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9.6
Q112
median14
Q315
95-th percentile18.4
Maximum22
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8130368
Coefficient of variation (CV)0.20251889
Kurtosis1.0246015
Mean13.890244
Median Absolute Deviation (MAD)1
Skewness0.24393619
Sum1291.7927
Variance7.913176
MonotonicityNot monotonic
2025-04-04T13:28:55.756504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
14 18
19.4%
13.8902439 11
11.8%
15 10
10.8%
13 9
9.7%
12 8
8.6%
11 8
8.6%
16 6
 
6.5%
17 5
 
5.4%
18 4
 
4.3%
10 4
 
4.3%
Other values (7) 10
10.8%
ValueCountFrequency (%)
6 1
 
1.1%
8 2
 
2.2%
9 2
 
2.2%
10 4
 
4.3%
11 8
8.6%
12 8
8.6%
13 9
9.7%
13.8902439 11
11.8%
14 18
19.4%
15 10
10.8%
ValueCountFrequency (%)
22 1
 
1.1%
21 2
 
2.2%
20 1
 
1.1%
19 1
 
1.1%
18 4
 
4.3%
17 5
 
5.4%
16 6
 
6.5%
15 10
10.8%
14 18
19.4%
13.8902439 11
11.8%

Weight
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3072.9032
Minimum1695
Maximum4105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size876.0 B
2025-04-04T13:28:55.861009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1695
5-th percentile2166
Q12620
median3040
Q33525
95-th percentile3976
Maximum4105
Range2410
Interquartile range (IQR)905

Descriptive statistics

Standard deviation589.89651
Coefficient of variation (CV)0.19196716
Kurtosis-0.85511567
Mean3072.9032
Median Absolute Deviation (MAD)475
Skewness-0.14366904
Sum285780
Variance347977.89
MonotonicityNot monotonic
2025-04-04T13:28:55.970019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3240 2
 
2.2%
3735 2
 
2.2%
3715 2
 
2.2%
3515 2
 
2.2%
2295 2
 
2.2%
3085 2
 
2.2%
2490 2
 
2.2%
3495 2
 
2.2%
2350 2
 
2.2%
3470 2
 
2.2%
Other values (71) 73
78.5%
ValueCountFrequency (%)
1695 1
1.1%
1845 1
1.1%
1965 1
1.1%
2045 1
1.1%
2055 1
1.1%
2240 1
1.1%
2270 1
1.1%
2285 1
1.1%
2295 2
2.2%
2325 1
1.1%
ValueCountFrequency (%)
4105 1
1.1%
4100 1
1.1%
4055 1
1.1%
4025 1
1.1%
4000 1
1.1%
3960 1
1.1%
3950 1
1.1%
3935 1
1.1%
3910 1
1.1%
3805 1
1.1%

Origin
Categorical

High correlation 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size876.0 B
USA
48 
non-USA
45 

Length

Max length7
Median length3
Mean length4.9354839
Min length3

Characters and Unicode

Total characters459
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownon-USA
2nd rownon-USA
3rd rownon-USA
4th rownon-USA
5th rownon-USA

Common Values

ValueCountFrequency (%)
USA 48
51.6%
non-USA 45
48.4%

Length

2025-04-04T13:28:56.080534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T13:28:56.158538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
usa 48
51.6%
non-usa 45
48.4%

Most occurring characters

ValueCountFrequency (%)
U 93
20.3%
S 93
20.3%
A 93
20.3%
n 90
19.6%
o 45
9.8%
- 45
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 93
20.3%
S 93
20.3%
A 93
20.3%
n 90
19.6%
o 45
9.8%
- 45
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 93
20.3%
S 93
20.3%
A 93
20.3%
n 90
19.6%
o 45
9.8%
- 45
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 93
20.3%
S 93
20.3%
A 93
20.3%
n 90
19.6%
o 45
9.8%
- 45
9.8%

Make
Text

Unique 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size876.0 B
2025-04-04T13:28:56.340557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length24
Median length20
Mean length13.763441
Min length7

Characters and Unicode

Total characters1280
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)100.0%

Sample

1st rowAcura Integra
2nd rowAcura Legend
3rd rowAudi 90
4th rowAudi 100
5th rowBMW 535i
ValueCountFrequency (%)
ford 8
 
4.3%
chevrolet 8
 
4.3%
dodge 6
 
3.2%
pontiac 5
 
2.7%
mazda 5
 
2.7%
volkswagen 4
 
2.2%
oldsmobile 4
 
2.2%
nissan 4
 
2.2%
hyundai 4
 
2.2%
toyota 4
 
2.2%
Other values (115) 134
72.0%
2025-04-04T13:28:56.661075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 104
 
8.1%
e 100
 
7.8%
93
 
7.3%
o 88
 
6.9%
r 81
 
6.3%
i 77
 
6.0%
t 58
 
4.5%
n 55
 
4.3%
l 54
 
4.2%
d 46
 
3.6%
Other values (49) 524
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 104
 
8.1%
e 100
 
7.8%
93
 
7.3%
o 88
 
6.9%
r 81
 
6.3%
i 77
 
6.0%
t 58
 
4.5%
n 55
 
4.3%
l 54
 
4.2%
d 46
 
3.6%
Other values (49) 524
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 104
 
8.1%
e 100
 
7.8%
93
 
7.3%
o 88
 
6.9%
r 81
 
6.3%
i 77
 
6.0%
t 58
 
4.5%
n 55
 
4.3%
l 54
 
4.2%
d 46
 
3.6%
Other values (49) 524
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 104
 
8.1%
e 100
 
7.8%
93
 
7.3%
o 88
 
6.9%
r 81
 
6.3%
i 77
 
6.0%
t 58
 
4.5%
n 55
 
4.3%
l 54
 
4.2%
d 46
 
3.6%
Other values (49) 524
40.9%

Interactions

2025-04-04T13:28:48.860818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:22.399162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:23.857755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:25.374785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:26.890014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:28.373460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:29.902261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.464608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.774539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:34.171116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:35.716928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:37.062634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:38.535575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:40.133633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:41.603987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:42.990350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:44.435343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:45.758872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:47.460695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:48.922817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:22.460000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:23.932354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:25.438481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:26.958473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:28.449565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:29.969951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.525691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.841064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:34.236809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:35.782499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:37.132741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:38.600087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:40.205178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:41.673745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:43.061960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:44.498015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:45.828872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:47.524781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:48.999409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:22.531814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:24.011434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:25.512560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:27.040119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:28.537114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:30.052537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.596046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.922066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-04T13:28:48.327066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:49.719291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:23.324279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:24.886163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:26.292597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:27.869989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:29.413650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.005729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.345050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:33.726221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:35.275230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:36.629941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:38.057869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:39.468613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:41.124012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:42.560676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:43.996557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:45.329620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:46.986180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:48.410934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:49.787783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:23.394193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:24.967789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:26.367344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:27.944670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:29.498182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.078326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.412148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:33.803482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:35.349831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:36.709415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:38.144694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:39.539614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:41.204635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:42.632585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:44.071370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:45.401047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:47.066863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:48.486757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:49.856957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:23.467887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:25.048370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:26.446028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:28.027207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:29.581697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.157092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.485740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:33.878095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:35.426002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:36.785940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:38.224792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:39.621626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:41.288230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:42.705294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:44.144285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:45.472883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:47.146948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:48.565431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:49.923649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:23.537727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:25.132095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:26.661083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:28.115220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:29.661250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.236251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.558445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:33.946811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:35.498540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:36.855430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:38.305804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:39.706955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:41.366919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:42.775054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:44.214869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:45.538885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:47.226493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:48.637532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:49.991301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:23.604630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:25.213179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:26.740873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:28.203434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:29.742529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.316251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.631443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:34.021812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:35.574159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:36.924529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:38.383522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:39.787481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:41.446430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:42.845044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:44.287682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:45.609503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:47.305180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:48.714008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:50.065032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:23.791157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:25.298282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:26.823964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:28.294871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:29.831196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:31.399794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:32.709992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:34.102353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:35.651934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:36.998188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:38.464757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:39.867117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:41.531450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:42.923680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:44.366430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:45.689175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:47.396286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T13:28:48.791767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-04-04T13:28:56.775393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AirBagsCylindersDriveTrainEngineSizeFuel.tank.capacityHorsepowerLengthLuggage.roomMPG.cityMPG.highwayMan.trans.availManufacturerMax.PriceMin.PriceOriginPassengersPriceRPMRear.seat.roomRev.per.mileTurn.circleTypeWeightWheelbaseWidthid
AirBags1.0000.2540.1640.2910.2400.3160.2040.3070.1250.1720.1110.2770.3340.3120.0000.1940.3160.0000.2850.1730.2200.3580.2860.2710.3450.000
Cylinders0.2541.0000.3030.6510.4920.6650.4810.4000.4980.4430.5650.2620.3210.4120.2870.3860.4180.3490.1930.4970.3580.3490.5430.3760.4570.224
DriveTrain0.1640.3031.0000.3110.3880.3160.3170.2290.1790.2100.0000.3350.2180.2850.0000.3820.2940.3280.0370.2000.0430.3700.3540.2080.4370.138
EngineSize0.2910.6510.3111.0000.7760.8140.8110.575-0.821-0.7260.6350.2120.6740.7620.3620.5030.727-0.5300.536-0.8110.8090.4550.8980.7890.873-0.300
Fuel.tank.capacity0.2400.4920.3880.7761.0000.7890.6580.509-0.884-0.8390.4820.0000.7580.8000.2760.4930.791-0.2740.558-0.5700.6470.5250.8940.7420.777-0.156
Horsepower0.3160.6650.3160.8140.7891.0000.6440.446-0.789-0.7100.3780.2120.8220.8630.0000.2440.858-0.0590.405-0.6770.6160.3500.8040.6050.739-0.244
Length0.2040.4810.3170.8110.6580.6441.0000.688-0.662-0.5470.6610.2770.5590.6700.4120.5450.619-0.4230.599-0.6310.7280.4880.7880.8230.796-0.271
Luggage.room0.3070.4000.2290.5750.5090.4460.6881.000-0.453-0.3370.4870.2100.4020.5220.3650.4740.463-0.3680.622-0.4980.4870.4710.5280.6420.524-0.116
MPG.city0.1250.4980.179-0.821-0.884-0.789-0.662-0.4531.0000.9360.4390.313-0.753-0.8010.084-0.494-0.7860.390-0.5140.688-0.6870.406-0.893-0.715-0.8100.179
MPG.highway0.1720.4430.210-0.726-0.839-0.710-0.547-0.3370.9361.0000.3470.291-0.692-0.7390.112-0.486-0.7230.316-0.4650.576-0.5930.427-0.838-0.632-0.6900.142
Man.trans.avail0.1110.5650.0000.6350.4820.3780.6610.4870.4390.3471.0000.4650.3270.4480.3950.6450.4140.4540.3750.6330.6270.7210.5570.6800.6170.342
Manufacturer0.2770.2620.3350.2120.0000.2120.2770.2100.3130.2910.4651.0000.4100.2560.8190.0000.4320.1780.0830.2110.0000.0000.0000.0000.0000.786
Max.Price0.3340.3210.2180.6740.7580.8220.5590.402-0.753-0.6920.3270.4101.0000.9230.1840.2330.984-0.0730.361-0.5050.5000.2760.7490.5860.605-0.112
Min.Price0.3120.4120.2850.7620.8000.8630.6700.522-0.801-0.7390.4480.2560.9231.0000.0820.2950.974-0.1470.472-0.5810.5330.4210.7920.6400.660-0.109
Origin0.0000.2870.0000.3620.2760.0000.4120.3650.0840.1120.3950.8190.1840.0821.0000.3780.1800.4970.2500.4530.4940.3130.0720.2530.4060.681
Passengers0.1940.3860.3820.5030.4930.2440.5450.474-0.494-0.4860.6450.0000.2330.2950.3781.0000.266-0.4660.695-0.3960.4990.6260.5880.7260.517-0.187
Price0.3160.4180.2940.7270.7910.8580.6190.463-0.786-0.7230.4140.4320.9840.9740.1800.2661.000-0.1080.422-0.5530.5280.4010.7810.6210.644-0.121
RPM0.0000.3490.328-0.530-0.274-0.059-0.423-0.3680.3900.3160.4540.178-0.073-0.1470.497-0.466-0.1081.000-0.2770.480-0.5320.267-0.416-0.440-0.5050.162
Rear.seat.room0.2850.1930.0370.5360.5580.4050.5990.622-0.514-0.4650.3750.0830.3610.4720.2500.6950.422-0.2771.000-0.4130.5040.4280.5850.6960.513-0.144
Rev.per.mile0.1730.4970.200-0.811-0.570-0.677-0.631-0.4980.6880.5760.6330.211-0.505-0.5810.453-0.396-0.5530.480-0.4131.000-0.7270.336-0.711-0.602-0.7560.192
Turn.circle0.2200.3580.0430.8090.6470.6160.7280.487-0.687-0.5930.6270.0000.5000.5330.4940.4990.528-0.5320.504-0.7271.0000.3550.7640.7020.833-0.280
Type0.3580.3490.3700.4550.5250.3500.4880.4710.4060.4270.7210.0000.2760.4210.3130.6260.4010.2670.4280.3360.3551.0000.5580.5250.4710.000
Weight0.2860.5430.3540.8980.8940.8040.7880.528-0.893-0.8380.5570.0000.7490.7920.0720.5880.781-0.4160.585-0.7110.7640.5581.0000.8620.876-0.211
Wheelbase0.2710.3760.2080.7890.7420.6050.8230.642-0.715-0.6320.6800.0000.5860.6400.2530.7260.621-0.4400.696-0.6020.7020.5250.8621.0000.796-0.240
Width0.3450.4570.4370.8730.7770.7390.7960.524-0.810-0.6900.6170.0000.6050.6600.4060.5170.644-0.5050.513-0.7560.8330.4710.8760.7961.000-0.285
id0.0000.2240.138-0.300-0.156-0.244-0.271-0.1160.1790.1420.3420.786-0.112-0.1090.681-0.187-0.1210.162-0.1440.192-0.2800.000-0.211-0.240-0.2851.000

Missing values

2025-04-04T13:28:50.179792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-04T13:28:50.467849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idManufacturerModelTypeMin.PricePriceMax.PriceMPG.cityMPG.highwayAirBagsDriveTrainCylindersEngineSizeHorsepowerRPMRev.per.mileMan.trans.availFuel.tank.capacityPassengersLengthWheelbaseWidthTurn.circleRear.seat.roomLuggage.roomWeightOriginMake
01AcuraIntegraSmall12.915.918.82531NoneFront41.814063002890Yes13.25177102683726.511.02705non-USAAcura Integra
12AcuraLegendMidsize29.233.938.71825Driver & PassengerFront63.220055002335Yes18.05195115713830.015.03560non-USAAcura Legend
23Audi90Compact25.929.132.32026Driver onlyFront62.817255002280Yes16.95180102673728.014.03375non-USAAudi 90
34Audi100Midsize30.837.744.61926Driver onlyFront62.817255002535Yes21.16193106703731.017.03405non-USAAudi 100
45BMW535iMidsize23.730.036.22230Driver onlyRear43.520857002545Yes21.14186109693927.013.03640non-USABMW 535i
56BuickCenturyMidsize14.215.717.32231Driver onlyFront42.211052002565No16.46189105694128.016.02880USABuick Century
67BuickLeSabreLarge19.920.821.71928Driver onlyFront63.817048001570No18.06200111744230.517.03470USABuick LeSabre
78BuickRoadmasterLarge22.623.724.91625Driver onlyRear65.718040001320No23.06216116784530.521.04105USABuick Roadmaster
89BuickRivieraMidsize26.326.326.31927Driver onlyFront63.817048001690No18.85198108734126.514.03495USABuick Riviera
910CadillacDeVilleLarge33.034.736.31625Driver onlyFront84.920041001510No18.06206114734335.018.03620USACadillac DeVille
idManufacturerModelTypeMin.PricePriceMax.PriceMPG.cityMPG.highwayAirBagsDriveTrainCylindersEngineSizeHorsepowerRPMRev.per.mileMan.trans.availFuel.tank.capacityPassengersLengthWheelbaseWidthTurn.circleRear.seat.roomLuggage.roomWeightOriginMake
8384ToyotaTercelSmall7.89.811.83237Driver onlyFront41.58252003505Yes11.9516294653624.011.0000002055non-USAToyota Tercel
8485ToyotaCelicaSporty14.218.422.62532Driver onlyFront42.213554002405Yes15.9417499693923.013.0000002950non-USAToyota Celica
8586ToyotaCamryMidsize15.218.221.22229Driver onlyFront42.213054002340Yes18.55188103703828.515.0000003030non-USAToyota Camry
8687ToyotaPreviaVan18.922.726.61822Driver only4WD42.413850002515Yes19.87187113714135.013.8902443785non-USAToyota Previa
8788VolkswagenFoxSmall8.79.19.52533NoneFront41.88155002550Yes12.4416393633426.010.0000002240non-USAVolkswagen Fox
8889VolkswagenEurovanVan16.619.722.71721NoneFront52.510945002915Yes21.17187115723834.013.8902443960non-USAVolkswagen Eurovan
8990VolkswagenPassatCompact17.620.022.42130NoneFront42.013458002685Yes18.55180103673531.514.0000002985non-USAVolkswagen Passat
9091VolkswagenCorradoSporty22.923.323.71825NoneFront62.817858002385Yes18.5415997663626.015.0000002810non-USAVolkswagen Corrado
9192Volvo240Compact21.822.723.52128Driver onlyRear42.311454002215Yes15.85190104673729.514.0000002985non-USAVolvo 240
9293Volvo850Midsize24.826.728.52028Driver & PassengerFront52.416862002310Yes19.35184105693830.015.0000003245non-USAVolvo 850